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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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library_name: transformers |
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pipeline_tag: token-classification |
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tags: |
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- deberta-v3 |
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datasets: |
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- DFKI-SLT/few-nerd |
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--- |
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## Deberta for Named Entity Recognition |
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I used a Pretrained Deberta-v3-base and finetuned it on Few-NERD, A NER dataset that contains over 180k examples and over 4.6 million tokens. |
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The Token labels are Person, Organisation, Location, Building, Event, Product, Art & Misc. |
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## How to use the model |
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```python |
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def print_ner(sentences): |
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"""Cleaning and printing NER results |
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""" |
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for sentence in sentences: |
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last_entity_type = sentence[0]['entity'] |
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last_index = sentence[0]['index'] |
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word = sentence[0]['word'] |
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for i, token in enumerate(sentence): |
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if (i > 0): |
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if (token['entity'] == last_entity_type) and (token['index'] == last_index + 1): |
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word = word + '' + token['word'] |
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else: |
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word = word.replace('▁', ' ') |
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print(f"{word[1:]} {last_entity_type}") |
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word = token['word'] |
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last_entity_type = token['entity'] |
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last_index = token['index'] |
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if i == len(sentence) - 1: |
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word = word.replace('▁', ' ') |
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print(f"{word[1:]} {last_entity_type}") |
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from transformers import pipeline |
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pipe = pipeline(model='RashidNLP/NER-Deberta') |
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sentence = pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"]) |
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print_ner(sentence) |
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``` |
